Toward a generalized Bienenstock-Cooper-Munro rule for spatiotemporal learning via triplet-STDP in memristive devices

Zeolite-Based Memristive Synapse with Ultralow Sub-10-fJ Energy Consumption for Neuromorphic Computation

The development of neuromorphic computation faces the appreciable challenge of implementing hardware with energy consumption on the level of a femtojoule per synaptic event to be comparable with the energy consumption of human brain. Controllable ultrathin conductive filaments are needed to achieve such extremely low energy consumption in memristive synapses but their formation is difficult to control owing to their stochastic morphology and unexpected overgrowth. Herein, a zeolite-based memristive synapse is demonstrated for the first time, in which Ag exchange in the sub-nanometer pore closely resembles synaptic Ca²⁺ dynamics across biomembrane channel. Particularly, the confined ultrasmall pore and low Ag ion migration barrier give the zeolite-based memristive synapse ultralow energy consumption below 10 fJ per synaptic spike, on par with the biological counterpart. Experimental results reveal that the gradual memristive effect is attributed to the dimension modulation of Ag clusters. In addition to emulating inherent cognitive functions through electrical stimulations, the experience-dependent transition of short-term plasticity to long-term plasticity using a chemical modulation method is achieved by treating the initial Ag quantity as a learning experience. The proposed memristors can be used to develop highly efficient memristive neural networks and are considered as a candidate for application in neuromorphic computation.

Self-Powered Memristive Systems for Storage and Neuromorphic Computing

A neuromorphic computing chip that can imitate the human brain’s ability to process multiple types of data simultaneously could fundamentally innovate and improve the von-neumann computer architecture, which has been criticized. Memristive devices are among the best hardware units for building neuromorphic intelligence systems due to the fact that they operate at an inherent low voltage, use multi-bit storage, and are cost-effective to manufacture. However, as a passive device, the memristor cell needs external energy to operate, resulting in high power consumption and complicated circuit structure. Recently, an emerging self-powered memristive system, which mainly consists of a memristor and an electric nanogenerator, had the potential to perfectly solve the above problems. It has attracted great interest due to the advantages of its power-free operations. In this review, we give a systematic description of self-powered memristive systems from storage to neuromorphic computing. The review also proves a perspective on the application of artificial intelligence with the self-powered memristive system.

Controlling Native Oxidation of HfS2 for 2D Materials Based Flash Memory and Artificial Synapse

Two-dimensional (2D) materials based artificial synapses are important building blocks for the brain-inspired computing systems that are promising in handling large amounts of informational data with high energy-efficiency in the future. However, 2D devices usually rely on deposited or transferred insulators as the dielectric layer, resulting in various challenges in device compatibility and fabrication complexity. Here, we demonstrate a controllable and reliable oxidation process to turn 2D semiconductor HfS₂ into native oxide, HfO_x, which shows good insulating property and clean interface with HfS₂. We then incorporate the HfO_x/HfS₂ heterostructure into a flash memory device, achieving a high on/off current ratio of ∼10⁵, a large memory window over 60 V, good endurance, and a long retention time over 10³ seconds. In particular, the memory device can work as an artificial synapse to emulate basic synaptic functions and feature good linearity and symmetry in conductance change during long-term potentiation/depression processes. A simulated artificial neural network based on our synaptic device achieves a high accuracy of ∼88% in MNIST pattern recognition. Our work provides a simple and effective approach for integrating high-k dielectrics into 2D material-based memory and synaptic devices.

Integration and Co-design of Memristive Devices and Algorithms for Artificial Intelligence

Memristive devices share remarkable similarities to biological synapses, dendrites, and neurons at both the physical mechanism level and unit functionality level, making the memristive approach to neuromorphic computing a promising technology for future artificial intelligence. However, these similarities do not directly transfer to the success of efficient computation without device and algorithm co-designs and optimizations. Contemporary deep learning algorithms demand the memristive artificial synapses to ideally possess analog weighting and linear weight-update behavior, requiring substantial device-level and circuit-level optimization. Such co-design and optimization have been the main focus of memristive neuromorphic engineering, which often abandons the “non-ideal” behaviors of memristive devices, although many of them resemble what have been observed in biological components. Novel brain-inspired algorithms are being proposed to utilize such behaviors as unique features to further enhance the efficiency and intelligence of neuromorphic computing, which calls for collaborations among electrical engineers, computing scientists, and neuroscientists.

Bienenstock-Cooper-Munro Learning Rule Realized in Polysaccharide-Gated Synaptic Transistors with Tunable Threshold

With reference to the organization of the human brain nervous system, a hardware-based approach that builds massively parallel neuromorphic circuits is of great significance to neuromorphic computing. The Bienenstock-Cooper-Munro (BCM) learning rule, which describes that the synaptic weight modulation exhibits frequency-dependent and tunable frequency threshold characteristics, is more compatible with the working principle of neuromorphic computing systems than spike-timing-dependent plasticity. Therefore, it is interesting to simulate the BCM learning rule on solid-state synaptic devices. Here, we have prepared λ-carrageenan (λ-car) electrolyte-gated oxide synaptic transistors, which exhibit good transistor performances, including a low subthreshold swing of 125 mV/dec, an on/off ratio larger than 10⁶, and a mobility of 9.5 cm² V^-1 s^-1. By modulating the initial channel current and spike frequency, the simulation of the BCM rule was successfully realized. The competitive relationship between the drift of protons under an electric field and the spontaneous diffusion of protons can explain this mechanism. The proposed λ-car-gated synaptic transistor has a great significance to neuromorphic computing.

Artificial visual systems enabled by quasi-two-dimensional electron gases in oxide superlattice nanowires

Rapid development of artificial intelligence techniques ignites the emerging demand on accurate perception and understanding of optical signals from external environments via brain-like visual systems. Here, enabled by quasi-two-dimensional electron gases (quasi-2DEGs) in InGaO₃(ZnO)₃ superlattice nanowires (NWs), an artificial visual system was built to mimic the human ones. This system is based on an unreported device concept combining coexistence of oxygen adsorption-desorption kinetics on NW surface and strong carrier quantum-confinement effects in superlattice core, to resemble the biological Ca²⁺ ion flux and neurotransmitter release dynamics. Given outstanding mobility and sensitivity of superlattice NWs, an ultralow energy consumption down to subfemtojoule per synaptic event is realized in quasi-2DEG synapses, which rivals that of biological synapses and now available synapse-inspired electronics. A flexible quasi-2DEG artificial visual system is demonstrated to simultaneously perform high-performance light detection, brain-like information processing, nonvolatile charge retention, in situ multibit-level memory, orientation selectivity, and image memorizing.

Advances of RRAM Devices: Resistive Switching Mechanisms, Materials and Bionic Synaptic Application

Resistive random access memory (RRAM) devices are receiving increasing extensive attention due to their enhanced properties such as fast operation speed, simple device structure, low power consumption, good scalability potential and so on, and are currently considered to be one of the next-generation alternatives to traditional memory. In this review, an overview of RRAM devices is demonstrated in terms of thin film materials investigation on electrode and function layer, switching mechanisms and artificial intelligence applications. Compared with the well-developed application of inorganic thin film materials (oxides, solid electrolyte and two-dimensional (2D) materials) in RRAM devices, organic thin film materials (biological and polymer materials) application is considered to be the candidate with significant potential. The performance of RRAM devices is closely related to the investigation of switching mechanisms in this review, including thermal-chemical mechanism (TCM), valance change mechanism (VCM) and electrochemical metallization (ECM). Finally, the bionic synaptic application of RRAM devices is under intensive consideration, its main characteristics such as potentiation/depression response, short-/long-term plasticity (STP/LTP), transition from short-term memory to long-term memory (STM to LTM) and spike-time-dependent plasticity (STDP) reveal the great potential of RRAM devices in the field of neuromorphic application.

High-Response, Ultrafast-Speed, and Self-Powered Photodetection Achieved in InP@ZnS-MoS2 Phototransistors with Interdigitated Pt Electrodes

Various hybrid zero-dimensional/two-dimensional (0D/2D) systems have been developed to fabricate phototransistors with better performance compared to two-dimensional (2D) layered materials as well as broaden potential applications. Herein, we integrated environment-friendly InP@ZnS core-shell QDs with high efficiency of light absorption and light-emitting properties with bilayer MoS₂ for the realization of 0D/2D mixed-dimensional phototransistors. Interdigitated (IDT) electrodes with Pt-patterned arrays, acting as light collectors as well as plasmonic resonators, can further enhance light harvesting from the InP@ZnS-MoS₂ hybrid phototransistors, contributing to achieving a photoresponsivity as high as 1374 A·W^-1. Moreover, thanks to the asymmetric Pt/MoS₂ Schottky junction at the source/drain contact, a self-powered characteristic with an ultrafast speed of 21.5 μs was achieved, which is among the best performances for 2D layered material-based phototransistors. In terms of these features, we demonstrated the artificial synapse network with short-time plasticity based on the self-powered photodetection device. Our work reveals the great potential of 0D/2D hybrid phototransistors for high-response, ultrafast-speed, and self-powered photodetectors coupled with artificial neuromorphic function.